Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Felipe Leite Coelho da Silva; Kleyton da Costa; Paulo Canas Rodrigues; Rodrigo Salas; Javier Linkolk López‐Gonzales

doi:10.3390/en15020588

Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Author(s)

Felipe Leite Coelho da Silva

Kleyton da Costa

Paulo Canas Rodrigues

Rodrigo Salas

Javier Linkolk López‐Gonzales

Date Issued

14 de enero de 2022

Type

Article

Volume

15

Issue

2

Start Page

588

End Page

588

DOI

10.3390/en15020588

Abstract

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.